Edge Computing refers to the practice of performing data processing and computation at the edge of the network, closer to the source of data. In traditional cloud computing, data is typically processed and stored in large centralized data centers. However, with the rise of Internet of Things (IoT) devices and the need for real-time processing and low-latency response times, there has been a growing interest in Edge Computing as well as other technologies.
The term "edge computing" is used because it refers to computing that takes place at the "edge" of the network. This means that instead of sending data to a centralized data center or cloud, where it would be processed and analyzed, the data is processed and analyzed closer to where it is generated. This is because there are certain applications that require low latency and high bandwidth, which may not be possible if the data needs to be sent to a central data center or cloud for processing. By performing the processing and analysis at the edge, these applications can operate more efficiently and with lower latency.
Now the question is, what is the main difference between Edge computing and traditional centralized data center? The main difference between Edge computing and a centralized big data center is the location of the computing resources. In a centralized big data center, all the processing and storage of data are done in a single location, which is usually a large data center. On the other hand, Edge computing involves the processing and storage of data at or near the source of data.
In a centralized big data center, data is transmitted from various sources to the data center for processing and analysis. This results in increased network traffic, latency, and bandwidth requirements. Moreover, the data center is responsible for the security and integrity of the data. As I mentioned earlier, Edge computing brings the computation closer to the data source. This reduces the amount of data that needs to be transmitted to a centralized data center, which in turn reduces network traffic, latency, and bandwidth requirements. It also provides real-time processing and analysis of data, allowing for faster and more efficient decision-making.
The simplest examples of Edge Computing applications are self-driving cars, which require real-time data processing and analysis, and smart factories, which rely on real-time monitoring and analysis of equipment performance data. The term has other range of potential use cases and applications, including:
- IoT (Internet of Things) devices: Edge computing can be used to process data generated by IoT devices, such as sensors and smart devices, in real time.
- Autonomous vehicles: Edge computing can be used to process the vast amounts of data generated by autonomous vehicles, such as lidar, radar, and video feeds, in real-time.
- Smart cities: Edge computing can be used to manage and process data generated by smart city infrastructure, such as traffic lights, parking meters, and environmental sensors.
- Healthcare: Edge computing can be used to process medical data generated by wearable devices, remote patient monitoring systems, and other healthcare devices in real time.
- Manufacturing: Edge computing can be used to optimize factory operations, such as predicting machine failures, improving quality control, and reducing downtime.
- Financial services: Edge computing can be used to process financial data in real time, such as credit card transactions and stock market data, to improve fraud detection and reduce latency.
To conclude, the goal of edge computing is to bring computing resources closer to the point where data is generated or consumed, in order to reduce latency, increase efficiency, and improve the overall user experience